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Let’s be real—building AI-powered apps in 2025 still feels a bit like duct-taping tools together. You've got your language model, your APIs, maybe some real-time data… and somehow you're supposed to make it all talk nicely? Yeah, we’ve been there.
At Dysnix, we work a lot with blockchain systems and MLOps pipelines, and we keep hitting the same wall: traditional integrations are messy, fragile, and not scalable. Every new tool feels like starting from scratch. That’s why, when we discovered Model Context Protocol (MCP)—an open standard for connecting LLMs to tools and data—it just clicked.
Think of MCP as the USB-C for AI: one clean way to plug your models into everything they need, from databases and APIs to local files and smart contracts. No hacks. No glue code.
In this guide, we’ll break down what MCP is, how it works, why it matters, and how you can start using it.
Here’s the thing: large language models are smart—but they’re also kind of stuck in a box.
Out of the gate, most LLMs don’t know anything beyond their training data. They can’t access real-time info, pull your latest transactions, check the weather, or trigger any action in the real world. And while tools like “function calling” and frameworks like LangChain tried to patch that, they only scratched the surface.
So, what do devs usually do? They build custom glue code. A database request here, a third-party API call there. You spin up a dozen scripts, slap on some error handling, hardcode a few auth tokens, and hope it all holds together when the model wants to fetch a record or run a process.
Sounds familiar?
The problem is that these integrations:
Even worse, you’re missing a shared protocol—a common language that both the AI and your systems can understand. Right now, we’re making LLMs talk to our services in 15 different dialects. No wonder it’s messy.
This is where MCP comes in. Instead of building yet another function wrapper, MCP offers a standardized, runtime-friendly way for AI to discover, describe, and use tools and data—safely and dynamically. It’s not just a patch. It’s a protocol.
And in the same way HTTP changed how we access the web, MCP is quietly redefining how AI talks to the outside world.
Model Context Protocol, or simply MCP, is an open protocol that lets large language models interact with tools, data sources, APIs, and other systems—all in a standardized way. Instead of hand-coding each integration, you connect your AI to a toolbox of capabilities that it can understand and use at runtime.
Think of MCP as a universal adapter between your LLM and the outside world.
It was first introduced by Anthropic (the team behind Claude) in late 2024, but it’s already being adopted beyond their ecosystem. MCP isn’t tied to any specific vendor—it’s open, flexible, and designed to scale. Whether you're building with Claude, an open-source model, or something else entirely, MCP is designed to fit into your stack.
Here’s the key idea: instead of hardcoding every possible tool or API, you run MCP servers—small services that describe what they can do (like fetch data, run actions, or return prompts) and expose that info to your AI model via a shared communication layer. Your LLM client (inside an app, chat interface, IDE—wherever it lives) connects to those servers, reads their capabilities, and uses them as needed.
Need to query a database? There’s an MCP server for that. Want to fetch the latest ETH price? Add a crypto API MCP server. Need your AI to write to a file or call a smart contract? You guessed it—just connect the right server.
Your model doesn’t need to be retrained. MCP tools are discoverable in real time, self-documented, and compatible with function-calling or prompt injection mechanisms that most LLMs already understand.
If LangChain is like a toolbox, MCP is the plug-and-play socket that makes every tool compatible—no matter who built it.
In short:
MCP is built on a clean, modular architecture that separates the AI model (the host) from the tools and data it interacts with (the servers). Between them sits the client, acting as a smart bridge. Everything talks over a persistent, bidirectional channel—usually via JSON-RPC 2.0 over WebSocket.
Here’s how it breaks down:
In practice, this could be:
It doesn’t matter where the model lives—as long as it can talk to the MCP client.
💡 Think of the host as the brain—it understands the user and figures out what action is needed.
The client is embedded in the host and handles the protocol layer. It’s what actually connects to MCP servers, negotiates capabilities, sends requests, and handles responses. You don’t have to build this from scratch—there are client libraries in Python, TypeScript, C#, etc.
💬 The client uses JSON-RPC to call tools, fetch data, or inject prompts—and parses the server’s replies back into a format the model understands.
Code snippet example (Python):
from modelcontext import MCPClient
client = MCPClient("ws://localhost:3923")
capabilities = client.describe()
print(capabilities["tools"]) # See what the server can doThe server is a self-contained service that wraps around a specific tool, dataset, or API. It exposes its capabilities—such as tools (functions), resources (data), and prompts (templates)—to the host via the protocol.
You can run multiple MCP servers in parallel, and each one can focus on a single domain:
Servers are self-describing, meaning they expose a manifest that tells the AI client what’s available and how to use it.
🧠 From the LLM’s point of view, it doesn’t matter what’s under the hood—it just sees a set of callable tools and readable data.
All communication happens over WebSocket + JSON-RPC 2.0. That gives us a stateful, low-latency, two-way connection—perfect for interactive sessions where the model might need to fetch data, wait, think, then take another action.
And unlike REST APIs, MCP isn’t limited to stateless requests—the connection stays alive, so sessions can have memory and context.
Once the model connects to an MCP server, the real magic begins. The server doesn’t just wait passively—it tells the model what it can do. This happens through a self-describing manifest that lists three main categories: resources, tools, and prompts. There’s also a fourth, more advanced feature—sampling.
Resources are read-only data sources. The model can query them, but not modify anything. Think of them as structured “windows” into some external state—like a table in a database, a file on disk, or a record in your CRM.
For example:
Resources are the eyes and ears of your model—what it can observe before deciding what to do next.
Tools are where the model actually gets to do things. They’re functions that execute logic: write to a file, send a message, trigger a transaction, or update a database.
Examples:
What’s important is that tools usually require explicit user approval. This keeps things safe and avoids situations where the model might run something sensitive without oversight.
Prompts are reusable instructions or templates that help structure how the model thinks about a task. They can inject a full pre-defined prompt like “Summarize this document using these steps...” or “Write a SQL query for this question…”
This is helpful when:
Think of prompts as “embedded strategies”—instead of rewriting instructions every time, you load one from the server and go.
Here’s where things get spicy. With sampling, the server can request the host’s LLM to generate completions. That’s right—the AI model doesn’t just pull data from the server; the server can also ask the model for help.
Example: a document processing MCP server could send the host model a long text and ask it to “summarize this paragraph,” or “extract the entities.” That output is then passed back to the server as part of a larger workflow.
This turns the AI into a remote cognitive service, usable by servers to complete tasks—a powerful feature for agent orchestration.
Every MCP server is self-documenting. When your client connects, it gets a manifest that looks something like this:
{
"tools": [
{
"name": "get_weather",
"description": "Fetches the weather forecast for a city",
"parameters": {
"type": "object",
"properties": {
"location": { "type": "string" }
},
"required": ["location"]
}
}
]
}
No need for external docs—the model (and your app) knows exactly what’s available and how to use it. This supports dynamic tool discovery, letting you plug in new capabilities without changing your AI logic.
Let’s say you’re building an AI assistant that helps users manage their crypto wallets. The goal? Let it read balances, check gas fees, and even send tokens — but only with the user’s explicit approval. You want it to be secure, explainable, and modular. With MCP, you can make this happen using a standard protocol instead of fragile glue code.
Here’s how the flow works from when your model needs to take action—like transferring tokens—to when it completes the task.
1. Connect to a server
The host (your AI app) uses the MCP client to connect to an MCP server. This could be a file server, a database connector, a blockchain RPC wrapper—you name it.
client = MCPClient("ws://localhost:4567")
When connected, the server responds with its manifest—a JSON list of tools, resources, prompts, and metadata. This is the handshake.
2. Read the capabilities
The MCP client now knows what this server can do—and shares that with the model. The model doesn’t need to guess or hallucinate APIs—it’s told exactly what’s available and how to use it.
The manifest might say:
3. Model plans an action
The user says something like: "Transfer 0.5 ETH to Alice."
The model, seeing the available tools, decides to call send_tokens(...).
It builds a structured request:
{
"tool_name": "send_tokens",
"args": {
"wallet": "0x123...",
"amount": 0.5,
"to": "0xabc..."
}
}
4. Request sent via MCP
The client sends this to the server over JSON-RPC. This is a live, persistent connection, so no need to start a new session each time.
Behind the scenes:
{
"jsonrpc": "2.0",
"method": "send_tokens",
"params": { ... },
"id": "req-774"
}
The server receives it and gets ready to act.
5. Server executes and responds
Once approved, the server runs the tool and returns the result:
{
"jsonrpc": "2.0",
"result": {
"tx_hash": "0xabcdef...",
"status": "success"
},
"id": "req-774"
}
If something fails—bad input, server error, timeout—an error object is returned instead.
6. Model uses the result
Now the model continues the conversation: "Done! I sent 0.5 ETH to Alice. Here’s the transaction hash: 0xabcdef..."
Or maybe it chains another call: "Want me to notify her on Slack?"
This iterative reasoning is where MCP shines—the model doesn’t just react, it acts and adapts with real-world feedback.
7. Loop or close
The session stays open as long as needed. The model might call multiple tools, read several resources, or even use prompts to reset context.
When the task is done, the connection can be closed—or stay idle for next time.
You might be wondering:
“Wait, isn’t this just another way to call APIs? I already do that with requests or fetch.”
Not quite. MCP doesn’t replace APIs—it standardizes how AI models discover, understand, and use them. And that’s a much bigger deal than it sounds.
Let’s compare.
Before MCP, integrating AI models with tools looked like this:
Basically: every integration is bespoke, and you’re the one keeping it all from falling apart.
MCP turns fragile, one-off integrations into a clean, modular, and scalable system. No more prompt hacking or custom wrappers—you spin up an MCP server, and your model instantly knows what tools are available. It’s like moving from hardwired circuits to plug-and-play components.
Each server is focused: one might expose a database, another a blockchain interface, and a third might expose a third‑party API. You can swap them anytime without changing your model logic. This modularity, inspired by microservices, makes MCP ideal for scaling AI systems.
Crucially, it’s secure by design. Sensitive actions like sending messages or transferring tokens require explicit user approval or policy-level permissions—giving you full control over what the model can and cannot do.
MCP also gives your model access to real-time context. Instead of relying on outdated training data, it can fetch fresh info—whether it’s weather, stock prices, or system logs—and make decisions based on the present moment.
And it works with any LLM. Claude, GPT, open-source—as long as your host supports the protocol, your tools are reusable across models, teams, and environments. One protocol. Many models. Zero reinvention.
At this point, you’re probably thinking: “Okay, this sounds great—but how do I actually use MCP in my stack?”
Good news: you don’t need to wait for a framework to catch up or roll your own from scratch. You can start right now, using open-source tooling that already exists.
The fastest way to get going is to plug into one of the many pre-built MCP servers available in the official registry. These servers wrap popular tools, APIs, and services and expose them in the MCP format.
Some examples:
Spin one up locally or in the cloud, and you’re ready to connect your AI model to real functionality.
Each server is standalone, so you can compose them like microservices—run just the ones you need.
To make your LLM aware of available tools, you’ll need an MCP client that connects the host (your app or model interface) to MCP servers.
Currently, clients are available for:
Once connected, the client handles:
This means you don’t have to manually describe tools to the model—they’re discovered and formatted automatically.
If you have custom tools or APIs you want to expose—say, internal databases, smart contracts, or domain-specific logic—you can create your own MCP server.
A basic server:
The protocol is intentionally lightweight, and you can build a working server with just a few hundred lines of code. Starter templates are available in Python, TypeScript, and more.
For local dev, you can run servers on localhost, use tunneling tools like ngrok, or connect over LAN. For production, MCP servers can run inside containers, on edge devices, or in secure cloud environments.
Since servers are stateless (aside from the WebSocket session), they scale horizontally and can be load-balanced like any other microservice.
And because the model never interacts directly with low-level systems—always through servers—your infrastructure remains clean, auditable, and secure.
Whether you’re building a small assistant or a fully autonomous agent, MCP gives you the foundation to connect models to the real world—safely, flexibly, and at scale.
Let’s look at a few standout use cases where MCP is already delivering tangible value—from developer tools to personal AI agents.
Who: Anthropic
What: Claude-powered desktop AI assistant
Problem: Users want AI to help with their daily workflow—drafting emails, summarizing files, checking calendars—but giving models access to personal data raises major privacy and security concerns.
How MCP helped: Claude Desktop uses MCP to connect the local Claude model to a set of sandboxed MCP servers running on the user’s machine. Each server exposes only specific, permissioned functionality—like reading files, listing emails, or accessing calendar events. Every action is gated by real-time user approval.
Result:
Who: Microsoft Semantic Kernel
What: Open-source SDK for building AI agents
Problem: Developers need AI agents that can interact with files, codebases, external APIs, and databases—but integrating these tools into an agent framework is typically time-consuming and inconsistent.
How MCP helped: Microsoft integrated MCP into Semantic Kernel, allowing devs to connect models to external tools through standard MCP servers. Developers can expose internal APIs, local tools, or even CI/CD scripts as callable functions—without having to write custom adapters or prompts.
Result:
Who: Leanware
What: AI-powered infrastructure agents
Problem: DevOps teams often lack structured ways to let AI observe system state, analyze logs, or trigger infra actions in a safe, audit-friendly way. Most LLM integrations require brittle wrappers or prompt injection.
How MCP helped: Leanware used MCP to wrap key internal tools (Kubernetes APIs, log access, monitoring dashboards) into MCP servers. Their LLM copilots could now request access to logs, receive alerts, suggest fixes, or even apply changes—all through well-scoped tool interfaces.
Result:
Model Context Protocol isn’t just another integration pattern—it’s the beginning of a new standard for how AI systems connect with the world around them.
We’ve seen this before: HTTP changed the web, USB changed hardware, and now MCP is quietly doing the same for AI. It brings structure, safety, and scalability to what used to be chaos—giving models the ability to observe, reason, and act in real-world environments, without fragile glue code or vendor lock-in.
As adoption grows, we expect to see:
Of course, challenges remain. We still need better tooling, improved onboarding, and more educational resources to help teams implement MCP at scale. But the core protocol is here—and it works.
At Dysnix, we're already exploring how MCP fits into our Web3 infrastructure, AI tooling, and MLOps pipelines. For us, it’s not just about smarter models—it’s about making those models actually useful in production.
If you’re thinking about integrating LLMs into your stack—whether it’s a crypto assistant, a backend agent, or an AI-powered internal tool—we’d love to help.
👉 Contact us at dysnix.com or just say hi on Twitter—we're always up for a good AI + infra chat.
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